Experiences of racism and racial disparities in health care among children and youth with autism and their caregivers: a systematic review
Bibliographic record
Abstract
PURPOSE: Although the health benefits of early diagnosis and therapeutic approaches for children and youth with autism spectrum disorder (ASD), racial disparities persist. This systematic review explored the experiences of racism and racial disparities in health care among children and youth with ASD and their caregivers. METHODS: We conducted a systematic review, drawing on six international databases. Two reviewers screened titles, abstracts, and full texts. Thirty-seven studies met our inclusion criteria and we applied a narrative synthesis to develop themes. RESULTS: Four themes were identified: (1) experiences and aspects of racism and racial disparities (i.e., language and cultural barriers, poor quality health care interactions, stereotypes and discrimination, family and community stigma, and indirect barriers); (2) racial disparities in health care (i.e., screening and referral, diagnosis, health care services, and care coordination and medication); (3) facilitators to accessing health care services; and (4) recommendations from caregivers. CONCLUSIONS: This review highlights the extensive racial disparities experienced by children with autism. More research is needed to explore youth's perspectives on racism in addition to exploring potential interventions to address racial disparities and improve health equity for youth with ASD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".